The present invention relates to bone segmentation and removal in medical image data, and more particularly, to automated removal of bone voxels from 3D computed tomography angiography images in order to visualize vessels in the 3D computed tomography angiography images.
Bone segmentation and removal in computed tomography angiography (CTA) is an important clinical application. CTA is a medical imaging technique that is often used to visualize the blood vessels in a patient's body. Computed tomography (CT) combines a series of X-ray images taken from different angles and uses computer processing to create cross-sectional images, or slices, of bones, blood vessels and soft tissues inside the body. The cross-sectional images, or slices, can be combined to generate 3D CT volumes. In CTA, a contrast agent is injected into the bloodstream of the patient prior to CT imaging in order to generate contrast enhanced CT images that visualize the patient's vessels. CTA volumes can be visualized using a volume rendering technique (VRT) so that clinicians are able to see 3D vascular structures and pathologies such as stenoses and aneurysms. The goal of bone removal is to segment and remove bone voxels from CTA volumes to yield a vascular-only view, which provides an unhindered 3D view of the vascular structures in the CTA volume.
In CTA images, there is an overlapping intensity between the distributions of bones and contrast enhanced vessels. That is bone and contrast enhance vessels appear with similar intensity in CTA images. Accordingly, bones can be a major obstacle in the visualization and analysis of vessel trees, aneurisms, and calcifications using CTA, and it is desirable to remove bone structures from CTA images in order to achieve a better visualization of the vessels. In the past, manual editing techniques have been used to extract and remove bone structures from the image data. However, the tedious and long operating time required for manual editing is prohibitive for it to be of practical use. Furthermore, due to image resolution and noise in the image data, bones and vessels with overlapping intensity distributions often appear to be connected, which creates significant challenges in automated segmentation and removal of bone structures from the image data.
Automatic bone removal in CTA volumes is a challenging task, due to the fact that many osseous and vascular structures have similar patterns in shape and contrast. Previous approaches generally can be categorized into top-down and bottom-up approaches. Top-down approaches include statistical shape model and image atlas based approaches. These approaches typically need strong initial information about the body region or landmark location, which make them difficult to apply in various field of views (FOV). In the bottom-up category, many approaches are designed for non-contrasted scans, including region-growing and super-voxel approaches. However, in CTA volumes, the existence of contrast agent enhances vessel intensities and significantly increases the complexity of the problem, and such approaches have difficulty is differentiating bone and non-bone regions in some regions in CTA volumes (e.g., subclavian) due to weak gradient and highly similar appearance of the bone and non-bone regions.